Field of the Invention
The invention relates to a system and a method for determining the effectiveness (overall equipment effectiveness (OEE)) of production installations, fault events and the causes of the fault events significantly contributing to losses in productivity.
Effectiveness is understood here as the concept of xe2x80x9cOverall Equipment Effectiveness, OEExe2x80x9d, which is described for example in the reference by Robert Hansen, titled xe2x80x9cLearning the Power of Overall Equipment Effectivenessxe2x80x9d, and in the 1999 conference report Machinery Reliability Conference and Exposition, titled xe2x80x9cThe Meeting of Machinery Reliability Mindsxe2x80x9d, April 12-14, Cincinnati, Ohio, pages 19 to 30, published by Industrial Communications, Inc., 1704 by Natalie Nehs Dr., Knoxville, Tenn. 37931.
OEE is accordingly a method for determining a percentage that indicates to what extent the actual productivity in each case reaches a planned, that is prescribed, productivity. OEE is also referred to as the multiplication of synergistic parameters, which define the xe2x80x9chealthxe2x80x9d of a process, to be specific OEE =availability xc3x97processing speed xc3x97quality.
For commercial reasons, and to safeguard product quality, operators of production installations have an interest in determining a desired effectiveness, which can be achieved in an undisturbed operation, and comparing the effectiveness at a given time with it. If the effectiveness at a given time deviates from the desired value, this indicates losses in productivity. It must then be determined which fault events exist and what is causing them. The causes may have their roots in physical, human or organizational areas.
Various methods and techniques can be used for the analysis of faults (in the sense of losses in productivity). The most important of these are failure modes and effects analysis (FMEA), fault tree analysis, or methods of statistical evaluation, such as for example the Pareto analysis [by John Moubray, RCM2, Butterworth-Heinemann, Second Edition 1997].
In the implementation of an FMEA, the following steps are taken:
a) functional breakdown of the installation;
b) description of main function and secondary function;
c) description and listing of functional fault statuses;
d) determination of all causes for each fault status;
e) determination of the effects on production objectives; and
f) quantitative assessment of the effects.
Fault tree analysis starts from an undesired TOP state. For this starting event, all the event situations that lead to this state are determined.
Statistical methods presuppose a suitable base of data from production. For example, with a Pareto analysis, those causes of faults that are responsible for the main production faults can be determined. FMEA and fault tree analysis can be supported by tools. Such tools guide the user step by step through the method, provide support in data acquisition and document the results.
The statistical methods presuppose, however, a suitable database, which is often not present. Either no data at all from production are recorded or else the information that would be necessary for a fault analysis is not acquired.
The methods mentioned above have their roots in engineering design, i.e. they are used for configuring a product or an installation to be as fail-safe as possible. The high standard of quality of the product reached justifies the considerable expenditure in terms of time and work for such analyses.
The xe2x80x98post-mortemxe2x80x99 analysis of losses and faults in a production installation is often time-critical, since the sustained loss in productivity entails considerable costs. A further disadvantage is that the methods do not support any procedure focusing on the cause of the fault at a given time.
It is known from the literature that there may be up to 7 cause levels between the fault events and the actual cause of the fault [John Moubray, RCM2, Butterworth-Heinemann, Second Edition 1997]. None of the known methods can indicate when the suitable level, which ensures lasting elimination of the cause of the fault, has been found.
It is accordingly an object of the invention to provide a system and a method for determining the effectiveness of production installations, fault events and the causes of faults which overcome the above-mentioned disadvantages of the prior art devices and methods of this general type, which make possible an automated determination of the effectiveness at a given time, fault events and the causes of faults.
With the foregoing and other objects in view there is provided, in accordance with the invention, a system for determining an effectiveness of production installations of various types, significant fault events which bring about deviations from a prescribed desired effectiveness, and causes of the significant fault events. The system contains a data acquisition device to be connected to a respective production installation and set up for continuous acquisition and ready-to-call-up storage of data including installation-related data and production-related data. A service device is connected to the data acquisition device. The service device includes an input device for inputting additional descriptive data including installation-related descriptive data and production-related descriptive data that cannot be called up from the data acquisition device, and a display device for displaying the effectiveness determined, the significant fault events and the causes of the significant fault events. An online system part is connected to and set up for calling up the installation-related data and the production-related data from the data acquisition device. The online system part has a fault event detector detecting the significant fault events on a basis of the data, the additional descriptive data input by the input device, and on an overall equipment effectiveness (OEE) script. The online system part determines the significant fault events by fault event evaluation using a configured assessment model, determines in each case the causes of the significant fault events using a configured fault model, and calculates the effectiveness. An offline system part is connected to the online system part. The offline system part contains or has access to a number of models including generic fault models and assessment models. The offline system part is set up for searching for the models on a basis of at least of called-up and/or input descriptive data. The offline system part configures the models and stores the models locally or in a locally distributed form. The offline system part is configured for storing the models in the online system part as the configured assessment model or the configured fault model.
The system according to the invention includes a service device, which is preferably configured as a mobile device and can be connected in each case to a data server in the master control system of a production installation. Both the method used and the implementation as a system are based on the use of pre-configured solution models. Such solution models can be established by an offline system part and be used in an online system part.
The service device can be used in an advantageous way for the analysis of causes in different production installations, for example both for the analysis of the causes of drops in productivity or inferior product quality in the making of paper and in filling installations for the filling of liquids in the food industry. This universal applicability is achieved by a series of generic models and by pre-configured assessment and fault models.
In accordance with an added feature of the invention, the respective production installation is a single machine or an installation having a number of machines.
In accordance with an additional feature of the invention, the data acquisition device is part of a master control system or a programmable controller.
In accordance with another feature of the invention, the service device is set up for using a web browser to access models which are a stored on a web server and for storing configured models there.
In accordance with a further feature of the invention, the online system part has an OEE calculator set up for calculating the effectiveness by using a stored OEE calculation formula.
In accordance with a further added feature of the invention, the fault event detector is set up for detecting the significant fault events by limit value monitoring the OEE script.
In accordance with a further additional feature of the invention, the online system part is set up for determining the significant fault events using a Pareto analysis and the configured assessment model. The online system part includes a cause determiner set up for determining causes of the significant event faults by using fault event data and the configured fault model or an expert system.
In accordance with another further feature of the invention, the service device is set up for determining recommendations for eliminating the significant faults events, visually presenting the significant fault events and/or outputting the significant fault events for further transmission.
In accordance with another added feature of the invention, the offline system part has a model searcher and a library storing the generic fault models for finding a best model. The best model is a fault model of which a fault event description is most similar to a respective search inquiry. The offline system part includes a model configurer and a model editor connected to the model configurer for configuring the generic fault models. The offline system part further includes a model generalizer for generalizing configured models and for storing the configured models in the library for reuse.
In accordance with another additional feature of the invention, the offline system part includes a model editor and with the aid of the model editor a search inquiry can be formulated for the model searcher.
With the foregoing and other objects in view there is provided, in accordance with the invention, a method for automatically determining an effectiveness of a production installation, significant fault events, and causes of the effectiveness deviating from a prescribed desired state. The method includes calling up productivity-relevant historical data acquired and stored by a data acquisition device connected to the production installation using a fault event detector, inputting additional data including installation-related data and production-related data, carrying out a continuous calculation of the effectiveness using an OEE calculator and a suitable method, performing an investigation of the data with regard to fault event patterns using a fault event detector, storing detected fault events as time series in a fault database, and identifying the significant fault events from the detected fault events using a fault event evaluation and a stored configured assessment model. The causes of faults are determined using a cause determiner with respect to a respective significant fault event, taking into account additionally input data containing a description of specific environmental conditions. The causes of faults determined and the effectiveness determined are presented visually and/or as a data output.
In accordance with an added mode of the invention, there is the step of carrying out the continuous calculation of the effectiveness using the OEE calculator and by accessing the fault events stored in the fault database.
In accordance with an additional mode of the invention, there is the step of editing additional fault events, which cannot be detected by the fault event detector using a configured OEE calculation script, using a fault event input.
In accordance with another mode of the invention, there are the steps of determining significant fault events using a fault event evaluator, and presenting visually the significant fault events in a Pareto diagram.
In accordance with a further mode of the invention, there are the steps of using a model searcher for searching by use of descriptive data stored in a model library for that generic model which best matches a specific fault event and the production installation, and feeding the generic model to a model editor and to a model configurer for forming configured models. The configured models are used for an evaluation of fault events and for a cause analysis.
In accordance with a further added mode of the invention, for determining the causes of faults by the cause determiner, cause hypotheses of a configured error model which contains cause-effect diagrams extending over a number of model levels are verified by the cause determiner using the descriptive data. The configured error model is worked step by step from one level to the next until an actual cause is found.
In accordance with a further additional mode of the invention, there is the step of using a fault model, which has a recommendation model added to it and with the aid of which recommendations for eliminating faults are determined and output.
In accordance with a concomitant feature of the invention, there are the steps of generalizing models configured in a course of the method by a model generalizer for later reuse resulting in generalized models, and storing the generalized models in a model library, elements of a respective model being one of generalized and removed.
Other features which are considered as characteristic for the invention are set forth in the appended claims.
Although the invention is illustrated and described herein as embodied in a system and a method for determining the effectiveness of production installations, fault events and the causes of faults, it is nevertheless not intended to be limited to the details shown, since various modifications and structural changes may be made therein without departing from the spirit of the invention and within the scope and range of equivalents of the claims.